59 lines
1.8 KiB
Markdown
59 lines
1.8 KiB
Markdown
---
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title: "Statistical Analysis"
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type: concept
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tags: [statistics, quality, data-science]
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sources: [testing-test-results-analyzer]
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last_updated: 2026-04-28
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---
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## Aliases
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- Statistical Methods
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- Quantitative Analysis
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- Confidence Interval Analysis
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## Definition
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统计分析——使用数学和统计方法从测试数据中提取洞察、验证假设、计算置信区间，确保所有结论有统计依据而非主观推断。
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## Core Methods (from TestResultsAnalyzer)
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```python
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import numpy as np
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from scipy import stats
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# 置信区间计算
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confidence_level = 0.95
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n = len(sample_data)
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mean = np.mean(sample_data)
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std_err = stats.sem(sample_data)
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ci = stats.t.interval(confidence_level, n-1, loc=mean, scale=std_err)
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# 假设检验
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t_stat, p_value = stats.ttest_ind(group_a, group_b)
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significant = p_value < 0.05
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# 相关性分析
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correlation, p_val = stats.pearsonr(x, y)
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```
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## Key Principles
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- **95% Confidence Level**：默认使用 95% 置信水平，所有结论必须报告置信区间。
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- **Statistical Significance Required**：相关性和差异分析必须伴随 p-value 检验。
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- **Cross-Validation**：重要结论需多数据源交叉验证。
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- **Methodology Documentation**：分析方法必须记录以确保可复现。
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## Key Rules (from TestResultsAnalyzer)
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> "Always use statistical methods to validate conclusions and recommendations"
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> "Provide confidence intervals and statistical significance for all quality claims"
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> "Base recommendations on quantifiable evidence rather than assumptions"
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## Connections
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- [[Quality-Metrics]]：统计方法是质量指标计算的基础。
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- [[Failure-Pattern-Analysis]]：失败模式识别依赖统计方法。
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- [[Defect-Prediction]]：预测模型需要统计验证。
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- [[Release-Readiness-Assessment]]：发布决策依赖统计置信度。
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- [[Quality-ROI-Analysis]]：ROI 计算需要统计方法。
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